Machine learning-based energy consumption clustering and forecasting for mixed-use buildings
© 2020 John Wiley & Sons Ltd Mixed-use buildings contribute to the sustainable development of cities by providing economic, environmental, and social benefits. Energy management of these buildings still remains a challenge due to their unpredictable energy consumption characteristics and the lac...
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oai:animorepository.dlsu.edu.ph:faculty_research-21062020-10-24T01:48:02Z Machine learning-based energy consumption clustering and forecasting for mixed-use buildings Culaba, Alvin B. Del Rosario, Aaron Jules R. Ubando, Aristotle T. Chang, Jo Shu © 2020 John Wiley & Sons Ltd Mixed-use buildings contribute to the sustainable development of cities by providing economic, environmental, and social benefits. Energy management of these buildings still remains a challenge due to their unpredictable energy consumption characteristics and the lack of design guidelines for energy efficiency and sustainability solutions. Energy consumption forecasting models have been crucial to the improvement of energy efficiency and sustainability of buildings but its application to mixed-use buildings are limited. Hence, this study aims to develop a prediction model to characterize and forecast the energy consumption of mixed-use buildings. Machine learning techniques are employed in the proposed prediction model which used k-means algorithm for clustering and support vector machines for forecasting. The prediction model was developed and demonstrated on simulated energy consumption of 30 mixed-use buildings from the open energy information database. The clustering results have found major differences in the consumption behavior of building clusters, especially on peaking characteristics. The differences were highlighted in terms of the domain knowledge on residential and commercial energy consumption behavior. The forecasting model results showed that the proposed integration of the clustering model was able to capture unique variations in the energy consumption of mixed-use buildings. This led to a 46% decrease in the mean bias error and a 10% decrease in the coefficient of variation root mean square error wherein both indicators are commonly used in building energy modeling standards. 2020-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/1107 Faculty Research Work Animo Repository |
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© 2020 John Wiley & Sons Ltd Mixed-use buildings contribute to the sustainable development of cities by providing economic, environmental, and social benefits. Energy management of these buildings still remains a challenge due to their unpredictable energy consumption characteristics and the lack of design guidelines for energy efficiency and sustainability solutions. Energy consumption forecasting models have been crucial to the improvement of energy efficiency and sustainability of buildings but its application to mixed-use buildings are limited. Hence, this study aims to develop a prediction model to characterize and forecast the energy consumption of mixed-use buildings. Machine learning techniques are employed in the proposed prediction model which used k-means algorithm for clustering and support vector machines for forecasting. The prediction model was developed and demonstrated on simulated energy consumption of 30 mixed-use buildings from the open energy information database. The clustering results have found major differences in the consumption behavior of building clusters, especially on peaking characteristics. The differences were highlighted in terms of the domain knowledge on residential and commercial energy consumption behavior. The forecasting model results showed that the proposed integration of the clustering model was able to capture unique variations in the energy consumption of mixed-use buildings. This led to a 46% decrease in the mean bias error and a 10% decrease in the coefficient of variation root mean square error wherein both indicators are commonly used in building energy modeling standards. |
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Culaba, Alvin B. Del Rosario, Aaron Jules R. Ubando, Aristotle T. Chang, Jo Shu |
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Culaba, Alvin B. Del Rosario, Aaron Jules R. Ubando, Aristotle T. Chang, Jo Shu Machine learning-based energy consumption clustering and forecasting for mixed-use buildings |
author_facet |
Culaba, Alvin B. Del Rosario, Aaron Jules R. Ubando, Aristotle T. Chang, Jo Shu |
author_sort |
Culaba, Alvin B. |
title |
Machine learning-based energy consumption clustering and forecasting for mixed-use buildings |
title_short |
Machine learning-based energy consumption clustering and forecasting for mixed-use buildings |
title_full |
Machine learning-based energy consumption clustering and forecasting for mixed-use buildings |
title_fullStr |
Machine learning-based energy consumption clustering and forecasting for mixed-use buildings |
title_full_unstemmed |
Machine learning-based energy consumption clustering and forecasting for mixed-use buildings |
title_sort |
machine learning-based energy consumption clustering and forecasting for mixed-use buildings |
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Animo Repository |
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2020 |
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https://animorepository.dlsu.edu.ph/faculty_research/1107 |
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